Local PatchMatch Based on Superpixel Cut for Efficient High-resolution Stereo Matching

Abstract: Obtaining the accurate disparity of each pixel quickly is the goal of stereo matching, but it is very difficult for the 3D labels-based methods due to huge search space of 3D labels, especially for highresolution images. We present an novel two-stage optimization strategy to get the accurate disparity map for high-resolution stereo image efficiently, which includes feature points optimization and superpixel optimization. In the first stage, we construct the support points including edge points and robust points for triangulation, which is used to extract feature points and then perform spatial propagation and random refinement to get the candidate 3D label sets. In the stage of superpixel optimization, we update per pixel labels of the corresponding superpixels using the candidate label sets, and then perform spatial propagation and random refinement. In order to provide more prior information to identify weak texture and textureless areas, we design the weight combination of "intensity + gradient + binary image" for constructing an optimal minimum spanning tree (MST) to compute the aggregated matching cost, and obtain the labels of minimum aggregated matching cost. We also design local patch surrounding the corresponding superpixel to accelerate our algorithm in parallel. The experimental result shows that our method achieves a good trade-off between running time and accuracy, including KITTI and Middlebury benchmark, which are the standard benchmarks for testing the stereo matching methods.

Dynamic Model for Assisted Reproductive Technology Outcome Prediction

Abstract Infertility is becoming a growing issue in almost all countries. Assisted Reproductive Technologies (ART) are recent development in treating infertility that give hope to the infertile couples. However, the pregnancy rates achieved with the aid of ART is considerably low, as success in ART is not only based on the treatment but also on many other controllable and uncontrollable biological, social, and environmental features. High expenditures and painful process of ART cycles are the two major barriers for opting for ART. Moreover, ART treatments are not covered by any health insurance schemes. Computational prediction models could be used to improve the success rate by predicting the treatment outcome, before the start of an ART cycle. This may suggest the couples and the doctors to decide on the next course of action i.e. either to opt for ART or opt for correcting determinants or quit the ART. With the intension to improve the success rate of ART by providing decision support system to the physicians as well to the patients before entering into the treatment this research work proposes a dynamic model for ART outcome prediction using Machine Learning (ML) techniques. The proposed dynamic model is partially implemented with the help of an ensemble of heterogeneous incremental classifier and its performance is compared with state-of-art classifiers such as Naïve Bayes (NB), Random Forest (RF), K-star etc.,using ART dataset. Performance of the model is evaluated with various metrics such as accuracy, Precision Recall Curve (PRC), Receiver Operating Characteristic (ROC), F-Measure etc., However, ROC cure area is taken as the chief metric. Evaluation results shows that the model achieves the performance with the ROC area value of 94.1 %.

An Enhanced Focused Web Crawler for Biomedical Topics Using Attention Enhanced Siamese Long Short Term Memory Networks

Abstract The Internet is chosen to be one among the primary source of biomedical information. To retrieve necessary biomedical information, the search engine needs an efficient, focused crawler mechanism. But the area of research concerned with the focused crawler for biomedical topics is notably scanty. However, the quantity, momentum, diversity, and quality of the available online biomedical information, challenges and calls for enhanced aid to crawl. This paper surmounts the challenges and proposes a new learning approach for focused web crawling adopting Attention Enhanced Siamese Long Short Term Memory (AE-SLSTM) Networks with peephole connections which predicts topical relevance of the web page. The proposed AE-SLSTM model accurately computes the semantic similarity between the topic and the web pages. The performance of the newly designed crawler is assessed using two well known metrics namely harvest rate ( h r a t e ) and irrelevance ratio ( p r a t e ). The presented crawler surpass the existing focused crawlers with an average h r a t e of 0.39 and an average p r a t e of 0.61 after crawling 5,000 web pages relating to biomedical topics. The results clearly depicts that the proposed methodology aids to download more relevant biomedical web pages related to the particular topic from the internet.

A Critique Empirical Evaluation of Relevance Computation for Focused Web Crawlers

Abstract Analogous to the spectacular growth of information-superhighway, The Internet, demands for coherent and economical crawling methods are translucent to shoot up. Consequently, many innovative techniques have been put forth for efficient crawling. Among them the significant one is focused crawlers. The focused crawlers are capable in searching web pages that are suitable for the topics defined in advance. Focused crawlers attract several search engines on the grounds of efficient filtering, reduced memory and time consumption. This paper furnishes a relevance computation based survey on web crawling. A bunch of fifty two focused crawlers from the existing literature survey is categorized to four different classes - classic focused crawler, semantic focused crawler, learning focused crawler and ontology learning focused crawler. The prerequisite and the mastery of each metric with respect to harvest rate, target recall, precision and F1-score are discussed. Future outlooks, shortcomings and strategies are also suggested.

Decision Tree Based Salp Swarm Optimization for Multi Medical Data Classification with Feature Reduction Technique

Abstract The ambitious task in the domain of medical informatics is medical data classification. From medical datasets, intention to ameliorate human burden with the medical data classification entails to taking in classification designs. The medical data classification is the major focus of this paper, where a Decision Tree based Salp Swarm Optimization (DT-SWO) algorithm is proposed. After pre-processingthe hybrid feature selection method selects the medical data features. The high dimensional features are reduced by Discriminant Independent Component Analysis (DICA) and DT-SWO is to classify the most relevant class of medical data. The details of four datasets namely Leukemia, Diffuse Larger B-cell Lymphomas (DLBCL), Lung cancer and Colon relating to four diseases for heart, liver, cancer and lungs are collected from the UCI machine learning repository. Ultimately, the experimental outcomes demonstrated that the proposed DT-SWO algorithm is suitable for medical data classification than other algorithms.

Prediction of Land Suitability for Crop Cultivation Using Classification Techniques

Abstract Agriculture, the backbone of every country, has been an emerging field of research, particularly in the recent past. The soil type and environment are critical factors that drive agriculture, especially in terms of crop prediction. To determine which crops grow best in certain types of soil and environment, the characteristics of the latter are to be ascertained. In the past, farmers picked suitable crops for cultivation, based on first-hand experience. Today, however, identifying appropriate crops for particular areas has become a difficult proposition. The application of machine learning techniques to agriculture is an emerging field of research that helps predicts crops for easy cultivation and improved productivity. In this work, a comparative analysis is undertaken using several classifiers like the k-Nearest Neighbor (kNN), Naïve Bayes (NB), Decision Tree (DT), Support Vector Machines (SVM), Random Forests (RF) and Bagging to help suggest the most suitable cultivable crop(s), based on soil and environmental characteristics, for a specific piece of land. The algorithms are trained with the training data and subsequently tested with the soil and climate-based test dataset. The results of all the approaches are evaluated to identify the best classification techniques. Experimental results show that the bagging method outclasses others with respect to all performance metrics.

Regression Imputation and Optimized Gaussian Naïve Bayes Algorithm for an Enhanced Diabetes Mellitus Prediction Model

Abstract Diabetes mellitus (DM) is a category of metabolic disorders caused by high blood sugar. The DM affects human metabolism, and this disease causes many complications like Heart disease, Neuropathy, Diabetic retinopathy, kidney problems, skin disorder and slow healing. It is therefore essential to predict the presence of DM using an automated diabetes diagnosis system, which can be implemented using machine learning algorithms. A variety of automated diabetes prediction systems have been proposed in previous studies. Even so, the low prediction accuracy of DM prediction systems is a major issue. This proposed work developed a diabetes mellitus prediction system to improve the diabetes mellitus prediction accuracy using Optimized Gaussian Naive Bayes algorithm. This proposed model using the Pima Indians diabetes dataset as an input to build the DM predictive model. The missing values of an input dataset are imputed using regression imputation method. The sequential backward feature elimination method is used in this proposed model for selecting the relevant risk factors of diabetes disease. The proposed machine learning classifier named Optimized Gaussian Naïve Bayes (OGNB) is applied to the selected risk factors to create an enhanced Diabetes diagnostic system which predicts Diabetes in an individual. The performance analysis of this prediction architecture shows that, over other traditional machine learning classifiers, the Optimized Gaussian Naïve Bayes achieves an 81.85% classifier accuracy. This proposed DM prediction system is effective as compared to other diabetes prediction systems found in the literature. According to our experimental study, the OGNB based diabetes mellitus prediction system is more appropriate for DM disease prediction.

A Generated Multi Branch Feature Fusion Model for Vehicle Re-identification

Abstract Vehicle re-id play a very import role in recent public safety, it has received more and more attention. The local features (e.g. hanging decorations and stickers) are widely used for vehicle re-id, but the same local feature exists in one perspective, but not exactly exists in other perspectives. In this paper, we firstly use experiments to verify that there is a low linear correlation between different dimension global features. Then we propose a new technique which uses global features instead of local features to distinguish the nuances between different vehicles. We design a vehicle re-identification method named a generated multi branch feature fusion method (GMBFF) to make full use of the complementarity between global features with different dimensions. All branches of the proposed GMBFF model are derived from the same model and there are only slight differences among those branches. Each of those branches can extract highly discriminative features with different dimensions. Finally, we fuse the features extracted by these branches. Existing research uses the fusing features for fusion and we use the global vehicle features for fusion. We also propose two different feature fusion methods which are single fusion method (SFM) and multi fusion method (MFM). In SFM, features for fusion with larger dimension occupy more weight in fused features. MFM overcomes the disadvantage of SFM. Finally, we carry out a lot of experiments on two widely used datasets which are VeRi-776 dataset and Vehicle ID dataset. The experimental results show that our proposed method is much better than the state-of-the-art vehicle re-identification methods.